Linear Econometrics

COURSES

INTRODUCTION TO LINEAR MODELS

General Information

Aim:

This course is predominantly an applied statistical course, with emphasis on statistical theory only when needed. It aims to provide the basic theoretical and operational concepts to the student about Linear Econometric Models of cross-section data. The course will cover estimation and inference principles, the mathematical (algebraic properties) of the Ordinary Least Square methods, simple and multiple linear regression models, tests for functional form and omitted variables, in addition to heteroskedasticity and weighted least squares. It will also emphasize the nature of residuals and analyze many of the inspection and tests of goodness-of-fit and influential measures by means of residuals. The empirical part of the course will be based on the R software and data from Wooldridge (2016).  I expect that students read the suggested literature specific to linear econometrics, including the basic texts on mathematical econometrics, probability, and statistical inference, as well participate in the data laboratory classes. At the end of the course I expect students to be able to manipulate cross-section data in R and apply the methods to specific areas of interest in Demography, Geography, Sociology, Economics, and Health Studies.

Tests and Grading:

  • Assignment 1: Estimation of a simple linear regression via OLS using Excel (20 points) [Download]
  • Assignment 2: Applied use of cross-section data to estimate, interpret and analyze the quality of the model (30 points)
  • Final test: a formal test covering the content of the course (50 points)

Tutoring:

Teaching Assistants:

Julia Calazans (Doctor Student in Demography)
julia-calazans

Otavio Miranda (Undergrad in Statistics)
otaviomiranda

Tutoring hours: Thursday, 11:00 am to 12:30 pm

More details: 

Download the complete syllabus here. Download pdf_icon

Data & Scripts

Data

Data Set Handbook.pdf_icon

Wooldridge_RDatar_symbol2

Datasets for Wooldridge Book (5th Edition) by Chapter on Cengage Website. r_symbol2

Datasets for Assignment 1. r_symbol2

 

Scripts

Class 1 – Simulation Probability Distributions in R.r_symbol2

Class 2 – Solution to the Computer Exercises (Chapter 1 – Wooldridge). r_symbol2

Class 3 – How to reproduce examples throughout the chapter (Chapter 2 – Wooldridge).r_symbol2

Class 4 – How to reproduce examples throughout the chapter (Chapter 3 – Wooldridge).r_symbol2
Class 4 – Solution to Computer Exercises (Chapter 2 – Wooldridge).r_symbol2
Class 4 – Frisch-Waugh-Lovell and Orthogonal Partitioned Regression Theorems (Simulation).r_symbol2

Class 5 – How to reproduce examples throughout the chapter (Chapter 4 – Wooldridge).r_symbol2
Class 5 – Solution to Computer Exercises (Chapter 3 – Wooldridge).r_symbol2
Class 5 – Central Limit Theorem and the Law of Large Numbers for convergence (Simulation).r_symbol2

Class 6 – How to reproduce examples throughout the chapter (Chapter 6 – Wooldridge).r_symbol2
Class 6 – Solution to Computer Exercises (Chapter 4 – Wooldridge).r_symbol2

Class 7 – How to reproduce examples throughout the chapter (Chapter 7 – Wooldridge).r_symbol2
Class 7 – Solution to Computer Exercises (Chapter 6 – Wooldridge).r_symbol2

Class 8 – How to reproduce examples throughout the chapter (Chapter 8 – Wooldridge).r_symbol2
Class 8 – Solution to Computer Exercises (Chapter 7 – Wooldridge).r_symbol2

Class 9 – Solution to Computer Exercises (Chapter 8 – Wooldridge).r_symbol2

Writing Materials, Powerpoints & Beamers

Class 1 – Review of Basic Terminology and Random Variables.pdf_icon
Class 2 – The Nature of Econometrics and Economic Data.pdf_icon
Class 5 – Asymptotic Theory.pdf_icon

Compulsory Reading (Textbook)

Chapter 1 – The nature of econometrics and economic data.pdf_icon
Chapter 2 – The simple regression model.pdf_icon
Chapter 3 – Multiple Regression Analysis: Estimation.pdf_icon
Chapter 4 – Multiple Regression Analysis: Inference. pdf_icon
Chapter 6 – Multiple Regression Analysis:Further Issues. pdf_icon
Chapter 7 – Multiple Regression Analysis with Qualitative Information: Binary (or Dummy) Variables. pdf_icon
Chapter 8 – Heteroskedasticity. pdf_icon

Weekly Assignments

Chapter 1 – Problems and Computer Exercises.pdf_icon
Chapter 2 – Problems and Computer Exercises.pdf_icon
Chapter 3 – Problems and Computer Exercises.pdf_icon
Chapter 4 – Problems and Computer Exercises.pdf_icon
Chapter 6 – Problems and Computer Exercises.pdf_icon
Chapter 7 – Problems and Computer Exercises.pdf_icon
Chapter 8 – Problems and Computer Exercises.pdf_icon
Chapter 9 – Problems and Computer Exercises.pdf_icon
Chapter 15 – Problems and Computer Exercises.pdf_icon

Teaching Assistant's Material

Introduction to R.r_symbol2

Basic R Manipulation (objects and basic functions).r_symbol2

PNAD in R. r_symbol2

Video Classes

Introduction to R and R Studio. youtubelogo

Extra Materials

References

Textbooks

Jeffrey M. Wooldridge Introductory Econometrics: A Modern Approach, 6th Edition, CENGAGE Learning, 2012.

Florian Heiss Using R for Introductory Econometrics, 1st Edition, Published using the independent publishing platform CreateSpace, 2016.

Same Level Reference Books

Kennedy, P. A Guide to Econometrics, Sixth Edition John Wiley & Sons, 2008.

Baum, C. An Introduction to Modern Econometrics Using Stata, Stata Press 2006.

Stock, J.H and M. W. Watson Introduction to Econometrics, 2nd ed., Addison-Wesley, 2006.

Hill, R. Carter, Griffths, William E. and Lim, Guay C. Principles of Econometrics, 3rd ed., John Wiley & Sons, 2008.

Advanced Readings

Goldberger, A. S. A Course in Econometrics 1st US Edition 4th Printing Edition, Harvard University Press, 2000.

Greene,W.H. Econometric Analysis, Seventh Edition, Pearson/Prentice Hall, 2012.

Woodridge, J. Econometric Analysis of Cross Section and Panel Data, 2nd Edition, MIT Press, 2010.

Long, S. and J. Freese Regression Models for Categorical and Limited Dependent Variables (Advanced Quantitative Techniques in the Social Sciences), 1nd Edition, Sage Publications, 1997.

Badi H. Baltagi Econometric Analysis of Panel Data, 4th Edition, Wiley, 2008.

James W. Hardin, Joseph M. Hilbe Generalized Linear Models and Extensions, 2nd Edition Stata Press, 2006.

Colin Cameron, Pravin K. Trivedi Regression Analysis of Count Data, Cambridge University Press, 1998.

John P. Hoffmann Generalized Linear Models: An Applied Approach, Pearson, 2004.

Cheng Hsiao Analysis of Panel Data, 2nd Edition, Cambridge University Press, 2003.